Developments in the management of flood defences and hydraulic infrastructure in the Netherlands

Jonkman, Sebastiaan N.; Voortman, H. G.; Klerk, Wouter Jan; Vuren, Saskia van

doi:10.1080/15732479.2018.1441317

Cited by 21 publications

(14 citation statements)

References 22 publications

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“…Machine learning can be used for structural health monitoring; this has applications in the preparation phase (Pyayt et al, 2014;Jonkman et al, 2018) and in the long-term reliability analysis required in the mitigation phase (Prendergast et al, 2018;Klerk et al, 2019). In the preparation phase for a flood, structural health monitoring is often done by manual inspections of the infrastructure on the ground.…”

Section: Predictive Impact Assessmentsmentioning

confidence: 99%

Invited perspectives: How machine learning will change flood risk and impact assessment

Wagenaar

Curran

Balbi

et al. 2020

Nat. Hazards Earth Syst. Sci.

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Abstract. Increasing amounts of data, together with more computing power and better machine learning algorithms to analyse the data, are causing changes in almost every aspect of our lives. This trend is expected to continue as more data keep becoming available, computing power keeps improving and machine learning algorithms keep improving as well. Flood risk and impact assessments are also being influenced by this trend, particularly in areas such as the development of mitigation measures, emergency response preparation and flood recovery planning. Machine learning methods have the potential to improve accuracy as well as reduce calculating time and model development cost. It is expected that in the future more applications will become feasible and many process models and traditional observation methods will be replaced by machine learning. Examples of this include the use of machine learning on remote sensing data to estimate exposure and on social media data to improve flood response. Some improvements may require new data collection efforts, such as for the modelling of flood damages or defence failures. In other components, machine learning may not always be suitable or should be applied complementary to process models, for example in hydrodynamic applications. Overall, machine learning is likely to drastically improve future flood risk and impact assessments, but issues such as applicability, bias and ethics must be considered carefully to avoid misuse. This paper presents some of the current developments on the application of machine learning in this field and highlights some key needs and challenges.

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Section: Predictive Impact Assessmentsmentioning

confidence: 99%

Invited perspectives: How machine learning will change flood risk and impact assessment

Wagenaar

Curran

Balbi

et al. 2020

Nat. Hazards Earth Syst. Sci.

View full text Add to dashboard Cite

show abstract

“…Machine learning can be used for structural health monitoring has applications in the preparation phase (Pyayt et al 2014, Jonkman et al 2018 and in the long-term reliability analysis required in the mitigation phase (Prendergast et al 2018. Structural health monitoring is in the preparation phase is often done by manual inspections of the infrastructure on the ground, for example in the Netherlands there is a large network of volunteers that can be activated in case of high river levels to inspect the dikes.…”

Section: Predictive Impact Assessmentsmentioning

confidence: 99%

Invited perspectives: How machine learning will change flood risk and impact assessment

Wagenaar¹,

Curran²,

Balbi³

et al. 2019

Preprint

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Abstract. Increasing amounts of data, together with more computing power and better machine learning algorithms to analyse the data are causing changes in almost every aspect of our lives. This trend is expected to continue as more data becomes available, computing power increases and machine learning algorithms improve. Flood risk and impact assessments are also being influenced by this trend, particularly in areas such as the development of mitigation measures, emergency response preparation, and flood recovery planning. Machine learning methods have the potential to improve accuracy as well as reduce calculating time and model development cost. It is expected that in the future more applications become feasible and many process models and traditional observation methods will be replaced by machine learning. Examples of this include the use of machine learning on remote sensing data to estimate exposure or on social media data to improve flood response. Some improvements may require new data collection efforts, such as for the modelling of flood damages or defence failures. In other fields, machine learning may not be suitable or should be applied complementary to process models, for example in hydrodynamic applications. Overall, machine learning is likely to drastically improve future flood risk and impact assessments, but issues such as applicability, bias and ethics must be considered carefully. This paper presents some of the current developments on the application of machine learning for flood risk and impact assessment, and highlights some key needs and challenges.

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“…Asset management for these requires a means to align reinforcement and renovations with maintenance and inspection efforts while ensuring that a variety of performance requirements required by society are met for the foreseeable future and beyond. Requirements can be acceptable risk levels, reliability targets or other factors such as the influence on existing ecosystems or the number of additional functions an asset provides [6]. The latter is especially of interest as many assets have multiple functions, for instance, nature-based solutions for coastal protection (e.g., salt marshes [7]) also provide ecosystem services.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Asset Management for Flood Protection: The FAIR Framework in Action

et al. 2020

Self Cite

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Uncertainties about climate change consequences, changing societal requirements and system complexity require flood protection asset managers to continuously evaluate their asset management policies and practice to manage risk and improve the resilience of their assets. However, there are many challenges in doing this, with asset operators often facing conflicting interests and major uncertainties about the future needs for asset performance. In the EU Interreg IV FAIR project, flood protection asset owners and operators, with scientific partners from the North Sea Region of Europe collaborated to develop practical guidance for adaptive asset management of flood protection infrastructure. The central component of this guidance is the FAIR framework, presented here. The framework combines insights and principles from ISO 55000 on asset management and ISO 14090 on climate adaptation with asset operator experiences to provide a practical guide for integration of asset management considerations within both strategic and operational contexts via a tactical handshake. This is a means to avoid the common lack of connection between strategic plans and operational practice. The applicability of the framework is illustrated with examples from Pilot Cases within the FAIR project, in which its value in terms of improved asset management and reduced costs has been demonstrated.

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Developments in the management of flood defences and hydraulic infrastructure in the Netherlands

Cited by 21 publications

References 22 publications

Invited perspectives: How machine learning will change flood risk and impact assessment

Invited perspectives: How machine learning will change flood risk and impact assessment

Invited perspectives: How machine learning will change flood risk and impact assessment

Adaptive Asset Management for Flood Protection: The FAIR Framework in Action

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